The chest x-ray is useful for detecting a number of patient conditions and for imaging a range of skeletal and organ structures. Radiographic images of the chest are useful for detection of lung nodules and other features that indicate lung cancer and other pathologic structures and other life-threatening conditions. In clinical applications such as in the Intensive Care Unit (ICU), chest x-rays can have particular value for indicating pneumothorax as well as for tube/line positioning, and other clinical conditions. To view the lung fields more clearly and allow more accurate analysis of a patient's condition, it is useful to accurately identify and suppress features of the chest x-ray, including the clavicle and the rib cage and related features, without losing detail of the lung tissue or of other features within the chest cavity.
Methods have been proposed for detecting and suppressing rib structures and allowing the radiologist to view the lung fields without perceptible obstruction by the ribs. Some methods have shown a measure of success using techniques for template matching, rib edge detection, or curve fitting edge detection. Even if rib structures are well-defined, however, it can be challenging to remove rib features from the chest x-ray image without degrading the underlying image content that can include lung tissue. Poor performance in detecting and suppressing the ribs translates to higher rates of false positives (FPs) in diagnosing the lung tissue and can cause the radiologist to misinterpret or overlook tissue features of interest.
Suppression of the clavicle presents a particular challenge for image processing and it can be more difficult to accurately identify the clavicle due to its particular structure and conventional chest x-ray imaging practices. In the x-ray image, the clavicle structure crosses the ribs and the intersection of the rib cage with the clavicle can readily confuse rib detection algorithms, leading to less than ideal rib segmentation and poor results in clavicle suppression.
US Patent Application Publication No. 2009/0290779 entitled “FEATURE-BASED NEURAL NETWORK REGRESSION FOR FEATURE SUPPRESSION” (Knapp) describes the use of a trained system or neural network for predicting the position and shape of rib components and subsequently subtracting the predicted rib components from the chest x-ray image.
US Patent Application Publication No. 2009/0060366 entitled “OBJECT SEGMENTATION IN IMAGES” (Worrell) describes techniques using detected rib edges to identify rib and clavicle structures.
“IMAGE-PROCESSING TECHNIQUE FOR SUPPRESSING RIBS IN CHEST RADIOGRAPHS BY MEANS OF MASSIVE TRAINING ARTIFICIAL NEURAL NETWORK (MTANN)” by Suzuki et al. in IEEE Transactions on Medical Imaging, Vol. 25 No. 4, April 2006 describes methods for detection of lung nodules and other features using learned results from a database to optimize rib suppression for individual patient images.
“DETECTION AND COMPENSATION OF RIB STRUCTURES IN CHEST RADIOGRAPHS FOR DIAGNOSE ASSISTANCE” in Proceedings of SPIE, 3338:774-785 (1998) by Vogelsang describes methods for compensating for rib structures in a radiographic image. Among techniques described in the Vogelsang et al. article are template matching and generation and selection from candidate parabolas for tracing rib edges.
“MODEL BASED ANALYSIS OF CHEST RADIOGRAPHS”, in Proceedings of SPIE 3979, 1040 (2000), by Vogelsang describes Bezier curve matching to find rib edges in a chest radiograph for alignment of a model and subsequent rib shadow compensation.
While some of these methods may have achieved a level of success using rib edge detection to identify rib structures that can then be suppressed in the x-ray image, there is a need to accurately detect and suppress the clavicle. Robustness is also desirable
Thus, there is a need for a method of clavicle suppression that accurately detects clavicles in chest x-ray images and suppresses the clavicle area in the image, meanwhile preserving the image content of underlying lung tissue.